In this paper, we have studied a conventional Factorization Machine over which we exercise tight control.
We have shown that user attributes do not always help recommendation and can harm coverage and diversity.
Our results point to the need for caution with user attributes.
The survival of user information into recommender output constitutes a privacy leak of the sort that has concerned [1]}, [2]}, but here measured without access to recommender-internal information.
Future work must avoid increasing the user signal in the recommendation list of a user without good cause in order to protect user privacy and respect data minimization.
